Multi-tier method of developing localized calibration models for non-invasive blood analyte prediction

ABSTRACT

A method of multi-tier classification and calibration in noninvasive blood analyte prediction minimizes prediction error by limiting co-varying spectral interferents. Tissue samples are categorized based on subject demographic and instrumental skin measurements, including in vivo near-IR spectral measurements. A multi-tier intelligent pattern classification sequence organizes spectral data into clusters having a high degree of internal consistency in tissue properties. In each tier, categories are successively refined using subject demographics, spectral measurement information and other device measurements suitable for developing tissue classifications. 
     The multi-tier classification approach to calibration utilizes multivariate statistical arguments and multi-tiered classification using spectral features. Variables used in the multi-tiered classification can be skin surface hydration, skin surface temperature, tissue volume hydration, and an assessment of relative optical thickness of the dermis by the near-IR fat band. All tissue parameters are evaluated using the NIR spectrum signal along key wavelength segments.

CROSS-REFERENCE TO RELATED APPLICATION

More than one reissue application has been filed for the reissue of U.S.Pat. No. 6,512,937. The reissue applications are application Ser. No.11/046,673 (the present application) and Ser. No. 11/065,223, all ofwhich are divisional reissues of U.S. Pat. No. 6,512,937. Thisapplication is a Continuation-in-part of U.S. patent application Ser.No. 09/359,191; filed on Jul. 22, 1999, now U.S. Pat. No. 6,280,381,which is incorporated herein in its entirety by this reference thereto.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to non-invasive blood analyte predication usingnear IR tissue absorption spectra. More particularly, the inventionrelates to a method of classifying sample spectra into groups having ahigh degree of internal consistency to minimized prediction error due tospectral interferents.

2. Description of Related Technology

The goal of noninvasive blood analyte measurement is to determine theconcentration of targeted blood analytes without penetrating the skin.Near infrared (NIR) spectroscopy is a promising noninvasive technologythat bases measurements on the absorbance of low energy NIR lighttransmitted into a subject. The light is focused onto a small area ofthe skin and propagates through subcutaneous tissue. The reflected ortransmitted light that escapes and is detected by a spectrometerprovides information about the contents of the tissue that the NIR lighthas penetrated and sampled. The absorption of light at each wavelengthis determined by the structural properties and chemical composition ofthe tissue. Tissue layers, each containing a unique heterogeneouschemistry and particulate distribution, result in light absorption andscattering of the incident radiation. Chemical components such as water,protein, fat and blood analytes absorb light proportionally to theirconcentration through unique absorption profiles. The sample tissuespectrum contains information about the targeted analyte, as well as alarge number of other substances that interfere with the measurement ofthe analyte. Consequently, analysis of the analyte signal requires thedevelopment of a mathematical model for extraction of analyte spectralsignal from the heavily overlapped spectral signatures of interferingsubstances. Defining a model that produces accurate compensation fornumerous interferents may require spectral measurements at one hundredor more frequencies for a sizeable number of tissue samples.

In equation 7, T is a matrix representing the concentration or magnitudeof interferents in all samples, and P represents the pure spectra of theinterfering substances or effects present. Any spectral distortion canbe considered an interferent in this formulation. For example, theeffects of variable sample scattering and deviations in optical samplingvolume must be included as sources of interference in this formulation.The direct calibration for a generalized least squares model on analytey isy_(GLS)=(K^(T) _(—) ⁻¹K)⁻¹K^(T) _(—) ⁻¹(x−k₀)  (8)where _ is defined as the covariance matrix of the interferingsubstances or spectral effects, Û is defined as the measurement noise, xis the spectral measurement, and k₀ is the instrument baseline componentpresent in the spectral measurement.

Accurate noninvasive estimation of blood analytes is also limited by thedynamic nature of the sample, the skin and living tissue of the patient.Chemical, structural and physiological variations occur produce dramaticchanges in the optical properties of the measured tissue sample. See R.Anderson, J. Parrish. The optics of human skin, Journal of InvestigativeDermatology, vol. 77(1), pp. 13-19 (1981); and W. Cheong, S. Prahl, A.Welch, A review of the optical properties of biological tissues, IEEEJournal of Quantum Electronics, vol. 26(12), pp. 2166-2185 (December1990); and D. Benaron, D. Ho, Imaging (NIRI) and quantitation (NIRS) intissue using time-resolved spectrophotometry: the impact of staticallyand dynamically variable optical path lengths, SPIE, vol. 1888, pp.10-21(1993); and J. Conway, K. Norris, C. Bodwell, A new approach for theestimation of body composition: infrared interactance, The AmericanJournal of Clinical Nutrition, vol. 40, pp. 1123-1140 (December 1984);and S. Homma, T. Fukunaga, A. Kagaya, Influence of adipose tissuethickness in near infrared spectroscopic signals in the measurement ofhuman muscle, Journal of Biomedical Optics, vol. 1(4), pp. 418-424(October 1996); and A. Profio, Light transport in tissue, AppliedOptics, vol. 28(12), pp. 2216-2222 (June 1989); and M. Van Gemert, S.Jacques, H. Sterenborg, W. Sta, Skin optics, IEEE Transactions onBiomedical Engineering, vol. 36(12), pp. 1146-1154 (December 1989); andB. Wilson, S. Jacques, Optical reflectance and transmittance of tissues:principles and applications, IEEE Journal of Quantum Electronics, vol.26(12), pp. 2186-2199.

Overall sources of spectral variations include the following generalcategories:

-   -   1. Co-variation of spectrally interfering species. The near        infrared spectral absorption profiles of blood analytes tend to        overlap and vary simultaneously over brief time periods. This        overlap leads to spectral interference and necessitates the        measurement of absorbance at more independently varying        wavelengths than the number of interfering species.    -   2. Sample heterogeneity. The tissue measurement site has        multiple layers and compartments of varied composition and        scattering. The spectral absorbance versus wavelength        measurement is related to a complex combination of the optical        properties and composition of these tissue components.        Therefore, the spectral response with changing blood analyte        concentration is likely to deviate from a simple linear model.    -   3. State Variations. Variations in the subject's physiological        state effect the optical properties of tissue layers and        compartments over a relatively short period of time. Such        variations, for example, may be related to hydration levels,        changes in the volume fraction of blood in the tissue, hormonal        stimulation, skin temperature fluctuations and blood hemoglobin        levels. Subtle variations may even be expected in response to        contact with an optical probe.    -   4. Structural Variations. The tissue characteristics of        individuals differ as a result of factors that include        hereditary, environmental influences, the aging process, sex and        body composition. These differences are largely anatomical and        can be described as slowly varying structural properties        producing diverse tissue geometry. Consequently, the tissue of a        given subject may have distinct systematic spectral absorbance        features or patterns that can be related directly to specific        characteristics such as dermal thickness, protein levels and        percent body fat. While the absorbance features may be        repeatable within a patient, the structural variations in a        population of patients may not be amenable to the use of a        single mathematical calibration model. Therefore, differences        between patients are a significant obstacle to the noninvasive        measurement of blood analytes through NIR spectral absorbance.

In a non-dispersive system, variations similar to (1) above are easilymodeled through multivariate techniques such as multiple linearregression and factor-based algorithms. Significant effort has beenexpended to model the scattering properties of tissue in diffusereflectance, although the problem outlined in (2) above has been largelyunexplored. Variation of the type listed in (3) and (4) above causessignificant nonlinear spectral response for which an effective solutionhas not been reported. For example, several reported methods ofnoninvasive glucose measurement develop calibration models that arespecific to an individual over a short period of time. See K. Hazen,Glucose determination in biological matrices using near-infraredspectroscopy, Doctoral Dissertation, University of Iowa (August 1995);and J. Burmeister, In vitro model for human noninvasive blood glucosemeasurements, Doctoral Dissertation, University of Iowa (December 1997);and M. Robinson, R. Eaton, D. Haaland, G. Koepp, E. Thomas, B. Stallardand P. Robinson, Noninvasive glucose monitoring in diabetic patients: apreliminary evaluation, Clin. Chem, vol. 38 (9), pp. 1618-1622 (1992).This approach avoids modeling the differences between patients andtherefore cannot be generalized to more individuals. However, thecalibration models have not been tested over long time periods duringwhich variation of type (4) may require recalibration. Furthermore, thereported methods have not been shown to be effective over a range oftype (3) variations.

SUMMARY OF THE INVENTION

The invention provides a Multi-Tier method for classifying tissueabsorbance spectra that localizes calibration and sample spectra intolocal groups that are used to reduce variation in sample spectra due toco-variation of spectral interferents, sample heterogeneity, statevariation and structural variation. Measurement spectra are associatedwith localized calibration models that are designed to produce the mostaccurate estimates for the patient at the time of measurement.Classification occurs through extracted features of the tissueabsorbance spectrum related to the current patient state and structure.

The invention also provides a method of developing localized calibrationmodels from tissue absorbance spectra from a representative populationof patients or physiological states of individual patients that havebeen segregated into groups. The groups or classes are defined on thebasis of structural and state similarity such that the variation intissue characteristics within a class is smaller than the variationbetween classes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a representation of a Multi-Tiered Classification Treestructure, according to the invention;

FIG. 2 is a block diagram of the architecture of an intelligent systemfor the noninvasive measurement of blood analytes, according to theinvention;

FIG. 3 is a block diagram of a pattern classification system, accordingto the invention;

FIG. 4 is a noninvasive absorbance spectrum collected using a diffusereflectance NIR spectrometer;

FIG. 5 shows the spectra of repeated noninvasive measurements with noattempt to control tissue hydration;

FIG. 6 shows the spectra of repeated noninvasive measurements usingambient humidity to control hydration, according to the invention;

FIG. 7 shows a noninvasive absorbance spectrum having a pronounced fatband at 1710 nm;

FIG. 8 is a block schematic diagram of a general calibration system formutually exclusive classes, according to the invention;

FIG. 9 is a block schematic diagram of a general calibration system forfuzzy class assignments, according to the invention; and

FIG. 10 is a block schematic diagram showing an example of parallelcalibration models for fuzzy set assignments, according to theinvention.

DETAILED DESCRIPTION

MULTI-TIERED CLASSIFICATION

The classification of tissue samples using spectra and other electronicand demographic information can be approached using a wide variety ofalgorithms. A wide range of classifiers exists for separating tissuestates into groups having high internal similarity: for example,Bayesian classifiers utilizing statistical distribution information; ornon-parametric neural network classifiers that assume little a prioriinformation. See K. Funkunaga, Intro to Statistical Pattern Recognition,Academic Pres, San Diego, Calif. (1990); and J. Hertz, A. Krogh, R.Palmer, Introduction To The Theory Of Neural Computation, Addison-WesleyPublishing Co., Redwood City, Calif. (1991). The multi-tieredclassification approach selected here provides the opportunity to growand expand the classification database as more data become available.The multi-tiered classifier is similar to a hierarchic classificationtree, but unlike a classification tree, the decision rules can bedefined by crisp or fuzzy functions and the classification algorithmused to define the decision rule can vary throughout the tree structure.

Referring now to FIG. 1, an example of a Multi-Tiered Classificationscheme is represented. A first tier 11 assigns sample spectra accordingto pre-defined age groups: 18-27 (15), 28-40 (14), 40-54 (13) and 55-80years old (12). As indicated, a sample has been assigned to the 28-40age group. A second tier 16 assigns samples to classes 18, 17 accordingto sex, in this case female. A third tier 19, groups according tostratum corneum hydration: 31-60 (20); <30 (21) and >61 corneometerunits (22); in this case, >61. A fourth tier 23, groups according toskin temperature: 88-90 (24); 86-88 (25); 84-86 and <84 degrees; in thiscase 84-86 degrees. In this way, a determination of class membership ismade within each tier in the multi-tiered structure. Finally, in a lasttier 28, a final class assignment is made into one of three pre-definedgroups 29, 30 and 31 according to relative optical thickness of thedermis.

For economy's sake, only the branching adjacent the selected classes iscompletely shown in FIG. 1, though there would be many more intermediateand final classification categories in a full multi-tieredclassification structure. For example, at the fourth tier 23 of Figure,there would be ninety-six possible classifications for a tissuemeasurement spectrum; at the final tier, there would be two hundredeighty-eight possible classifications. The foregoing description of aMulti-Tier Classification structure is meant to be exemplary only. Oneskilled in the art will appreciate that an actual classificationstructure could have more or fewer tiers, and different decision rulescould be utilized at each tier than have been utilized in the example.

FEATURE EXTRACTION

As previously indicated, at each tier in the classification structure,classification is made based on a priori knowledge of the sample, or onthe basis of instrumental measurements made at the tissue measurementsite. In the example of FIG. 1, the first two tiers utilize a prioriinformation about the sample: subject age and sex. Successive tiersutilize information gained from instrumental measurements at the tissuemeasurement site. Further classification occurs on the basis ofextracted features from the tissue absorbance spectra themselves.

Feature extraction is any mathematical transformation that enhances aquality or aspect of the sample measurement for interpretation. See R.Duda, P. Hart, Pattern Classification and Scene Analysis, John Wiley andSons, New York (1973). FIG. 2 shows a block diagram of an intelligentmeasurement system for noninvasive blood analyte prediction, fullydescribed in the parent application to the current application: S. Malinand T. Ruchti, An Intelligent System For Noninvasive Blood AnalytePrediction, U.S. patent application Ser. No. 09/359,191; Jul. 22, 1999,The purpose of feature extraction 41 in FIG. 2 is to concisely representthe structural properties and physiological state of the tissuemeasurement site. The set of features is used to classify the patientand determine the calibration model(s) most useful for blood analyteprediction.

The features are represented in a vector, zε

^(M) that is determined from the preprocessed measurement throughz=f(λ,x)  (1)where f:

^(N)→R^(M) is a mapping from the measurement space to the feature space.Decomposing f(•) will yield specific transformations, f_(i)(•):

^(N)→

^(M) _(i) for determining a specific feature. The dimension, M_(i),indicates whether the i^(th) feature is a scalar or a vector and theaggregation of all features is the vector z. When a feature isrepresented as a vector or a pattern, it exhibits a certain structureindicative of an underlying physical phenomenon.

The individual features are divided into two categories:

-   -   1. abstract and    -   2. simple.

Abstract features do not necessarily have a specific interpretationrelated to the physical system. Specifically, the scores of a principalcomponent analysis are useful features although their physicalinterpretation is not always known. The utility of the principalcomponent analysis is related to the nature of the tissue absorbancespectrum. The most significant variation in the tissue spectralabsorbance is not caused by a blood analyte but is related to the state,structure and composition of the measurement site. This variation ismodeled by the primary principal components. Therefore, the leadingprincipal components tend to represent variation related to thestructural properties and physiological state of the tissue measurementsite. Simple features are derived from an a priori understanding of thesample and can be related directly to a physical phenomenon. Usefulfeatures that can be calculated from NIR spectral absorbancemeasurements include but are not limited to:

-   -   1. Thickness of adipose tissue. See J. Conway, K. Norris, C.        Bodwell, A new approach for the estimation of body composition:        infrared interactance, The American Journal of Clinical        Nutrition, vol. 40, pp. 1123-1140 (December 1984) and S.        Homma, T. Fukunaga, A. Kagaya, Influence of adipose tissue        thickness in near infrared spectroscopic signals in the        measurement of human muscle, Journal of Biomedical Optics,        vol.1(4), pp. 418-424 (October 1996).    -   2. Tissue hydration. See K. Martin, Direct measurement of        moisture in skin by NIR spectroscopy, J. Soc. Cosmet. Chem.,        vol. 44, pp. 249-261 (September/October 1993).    -   3. Magnitude of protein absorbance. See J. Conway, et al.,        supra.    -   4. Scattering properties of the tissue. See A. Profio, Light        transport in tissue, Applied Optics, vol. 28(12), pp. 2216-2222        (June 1989) and W. Cheong, S. Prahl, A. Welch, A review of the        optical properties of biological tissues, IEEE Journal of        Quantum Electronics, vol. 26(12), pp. 2166-2185 (December 1990);        and R. Anderson, J. Parrish. The optics of human skin, Journal        of Investigative Dermatology, vol. 77(1), pp. 13-19 (1981).    -   5. Skin thickness. See Anderson, et al., supra; and Van Gemmert,        et al., supra.    -   6. Temperature related effects. See Funkunga, supra.    -   7. Age related effects. See W. Andrew, R. Behnke, T. Sato,        Changes with advancing age in the cell population of human        dermis, Gerontologia, vol. 10, pp. 1-19 (1964/65); and W.        Montagna, K. Carlisle, Structural changes in aging human skin,        The Journal of Investigative Dermatology, vol. 73, pp. 47-53        (1979; and 19 J. Brocklehurst, Textbook of Geriatric Medicine        and Gerontology, pp.593-623, Churchill Livingstone, Edinburgh        and London (1973).    -   8. Spectral characteristics relates to sex. See T. Ruchti,        Internal Reports and Presentations, Instrumentation Metrics,        Inc.    -   9. Pathlength estimates. See R. Anderson, et al., supra and S.        Matcher, M. Cope, D. Delpy, Use of water absorption spectrum to        quantify tissue chromophore concentration changes in        near-infrared spectroscopy, Phys.    -   Med. Biol., vol. 38, pp. 177-196 (1993).    -   10. Volume fraction of blood in tissue. See Wilson, et al.,        supra.    -   11. Spectral characteristics related to environmental        influences.

Spectral decomposition is employed to determine the features related toa known spectral absorbance pattern. Protein and fat, for example, haveknown absorbance signatures that can be used to determine theircontribution to the tissue spectral absorbance. The measuredcontribution is used as a feature and represents the underlying variablethrough a single value.

Features relates to demographic information, such as age, arecombinations of many different effects that cannot be represented by asingle absorbance profile. Furthermore, the relationship of demographicvariables and the tissue spectral absorbance is not deterministic. Forexample, dermal thickness and many other tissue properties arestatistically related to age but also vary substantially as a result ofhereditary and environmental influences. Therefore, factor based methodsare employed to build models capable of representing variation in themeasured absorbance related to the demographic variable. The projectionof a measured absorbance spectrum onto the model constitutes a featurethat represents the spectral variation related to the demographicvariable. The compilation of the abstract and simple featuresconstitutes the M-dimensional feature space. Due to redundancy ofinformation across the set of features, optimum feature selection and/ordata compression is applied to enhance the robustness of the classifier.

CLASSIFICATION

The goal of feature extraction is to define the salient characteristicsof measurements that are relevant for classification. Feature extractionis performed at branching junctions of the multi-tiered classificationtree structure. The goal of the classification step is to assign thecalibration model(s) most appropriate for a particular noninvasivemeasurement. In this step the patient is assigned to one of manypredefined classes for which a calibration model has been developed andtested. Since the applied calibration model is developed for similartissue absorbance spectra, the blood analyte predictions are moreaccurate than those obtained from a universal calibration model.

As depicted in FIG. 3, pattern classification generally involves twosteps:

-   -   1. a mapping step in which a classification model 53 measures        the similarity of the extracted features to predefined classes;        and    -   2. an assignment step in which a decision engine 54 assigns        class membership. Within this framework, two general methods of        classification are proposed. The first uses mutually exclusive        classes and therefore assigns each measurement to one class. The        second scheme utilizes a fuzzy classification system that allows        class membership in more than one class simultaneously. Both        methods rely on previously defined classes, as described below.        Class Definition

The development of the classification system requires a data set ofexemplar spectral measurements from a representative sampling of thepopulation. Class definition is the assignment of the measurements inthe exploratory data set to classes. After class definition, themeasurements and class assignments are used to determine the mappingfrom the features to class assignments.

Class definition is performed through either a supervised or anunsupervised approach. See Y. Pao, Adaptive Pattern Recognition andNeural Networks, Addison-Wesley Publishing Co., Reading, Mass. (1989).In the supervised case, classes are defined through known differences inthe data. The use of a priori information in this manner is the firststep in supervised pattern recognition, which develops classificationmodels when the class assignment is known. For example, the majority ofobserved spectral variation can be modeled by three abstract factors,which are related to several physical properties including body fat,tissue hydration and skin thickness. Categorizing patients on the basisof these three features produces eight different classes if each featureis assigned a “high” and “low” value. The drawback to this approach isthat attention is not given to spectral similarity and the number ofclasses tends to increase exponentially with the number of features.

Unsupervised methods rely solely on the spectral measurements to exploreand develop clusters or natural groupings of the data in feature space.Such an analysis optimizes the within cluster homogeneity and thebetween cluster separation. Clusters formed from features with physicalmeaning can be interpreted based on the known underlying phenomenoncausing variation in the feature space. However, cluster analysis doesnot utilize a priori information and can yield inconsistent results.

A combination of the two approaches utilizes a priori knowledge andexploration of the feature space for naturally occurring spectralclasses. In this approach, classes are first defined from the featuresin a supervised manner. Each set of features is divided into two or moreregions and classes are defined by combinations of the featuredivisions. A cluster analysis is performed on the data and the resultsof the two approaches are compared. Systematically, the clusters areused to determine groups of classes that can be combined. Afterconglomeration, the number of final class definitions is significantlyreduced according to natural divisions in the data. Subsequent to classdefinition, a classifier is designed through supervised patternrecognition. A model is created, based on class definitions, thattransforms a measured set of features to an estimated classification.Since the ultimate goal of the classifier is to produce robust andaccurate calibration models, an iterative approach must be followed inwhich class definitions are optimized to satisfy the specifications ofthe measurement system.

Statistical Classification

The statistical classification methods are applied to mutually exclusiveclasses whose variation can be described statistically. See J. Bezdek,S. Pal, eds, Fuzzy Models for Pattern Recognition, IEEE Press,Piscataway, N.J. (1992). Once class definitions have been assigned to aset of exemplary samples, the classifier is designed by determining anoptimal mapping or transformation from the feature space to a classestimate which minimizes the number of misclassifications. The form ofthe mapping varies by method as does the definition of “optimal”.Existing methods include linear Discriminant analysis, SIMCA, knearest-neighbor and various forms of artificial neural networks. SeeFunkunaga, supra; and Hertz, et al., supra; and Martin, supra; and Duda,et al., supra; and Pao, supra; and S. Wold, M. Sjostrom, SIMCA: A methodfor analyzing chemical data in terms of similarity and analogy,Chemometrics: Theory and Application, ed. B. R. Kowalski, ACS SymposiumSeries, vol. 52 (1977); and S. Haykin, Neural Networks: A ComprehensiveFoundation, Prentice-Hall, Upper Saddle River. N.J. (1994). The resultis a function or algorithm that maps the feature to a class, c,according toc=f(z)  (2 1)where c is an integer on the interval [1,P] and P is the number ofclasses. The class is used to select or adapt the calibration model asdiscussed in the Calibration Section.Fuzzy Classification

While statistically based class definitions provide a set of classesapplicable to blood analyte estimation, the optical properties of thetissue sample resulting in spectral variation change over a continuum ofvalues. Therefore, the natural variation of tissue thickness, hydrationlevels and body fat content, among others, results in class overlap.Distinct class boundaries do not exist and many measurements are likelyto fall between classes and have a statistically equal chance ofmembership in any of several classes. Therefore, “hard” class boundariesand mutually exclusive membership functions appear contrary to thenature of the target population.

A more versatile method of class assignment is based on fuzzy settheory. See Bezdek, et al., supra; and C. Chen, ed., Fuzzy Logic andNeural Network Handbook, IEEE Press, Piscataway, N.J. (1996); and L.Zadeh, Fuzzy Sets, Inform. Control, vol. 8, pp. 338-353 (1965).Generally, membership in fuzzy sets is defined by a continuum of gradesand a set of membership functions that map the feature space into theinterval [0,1] for each class. The assigned membership grade representsthe degree of class membership with “1” corresponding to the highestdegree. Therefore, a sample can simultaneously be a member of more thanone class.

The mapping from feature space to a vector of class memberships is givenbyc_(k)=f_(k)(z)  (2)where k=1,2, . . . P, f_(k)(•) is the membership function of the k^(th)class, c_(k)ε[0,1] for all k and the vector cε

^(P) is the set of class memberships. The membership vector provides thedegree of membership in each of the predefined classes and is passed tothe calibration algorithm.

The design of membership functions utilizes fuzzy class definitionssimilar to the methods previously described. Fuzzy cluster analysis canbe applied and several methods, differing according to structure andoptimization approach can be used to develop the fuzzy classifier. Allmethods attempt to minimize the estimation error of the class membershipover a population of samples.

MULTI-TIERED CALIBRATION

Blood analyte prediction occurs by the application of a calibrationmodel to the preprocessed measurement as depicted in FIG. 2. Theproposed prediction system involves a calibration or a set ofcalibration models that are adaptable or selected on the basis of theclassification step.

DEVELOPMENT OF LOCALIZED CALIBRATION MODELS

Accurate blood analyte prediction requires calibration models that arecapable of compensating for the co-varying interferents, sampleheterogeneity, state and structural variations encountered. Complexmixtures of chemically absorbing species that exhibit substantialspectral overlap between the system components are solvable only withthe use of multivariate statistical models. However, prediction errorincreases with increasing variation in interferents that also co-varywith analyte concentration in calibration data. Therefore, blood analyteprediction is best performed on measurements exhibiting smallerinterference variations that correlate poorly with analyte concentrationin the calibration set data. Since it may not be possible to make allinterference variations random, it is desirable to limit the range ofspectral interferent variation in general.

The principle behind the multi-tiered classification and calibrationsystem is based on the properties of a generalized class of algorithmthat are required to compensate for overlapped interfering signals inthe presence of the desired analyte signal. See H. Martens, T. Naes,Multivariate Calibration, John Wiley and Sons, New York (1989). Themodels used in this application require the measurement of multipleindependent variables, designated as x, to estimate a single dependentvariable, designated as y. For example, y may be tissue glucoseconcentration, and x may represent a vector, [x₁ x₂ . . . x_(i)],consisting of the noninvasive spectrum signal intensities at each of nwavelengths.

The generalized form of a model to be used in the calculation of asingle glucose estimate uses a weighted summation of the noninvasivespectrum as in Equation 4. The weights, w, are referred to as theregression vector.y=Σ_(w) _(i) _(x) _(i)   (4)

The weights define the calibration model and must be calculated from agiven calibration set of noninvasive spectra in the spectral matrix X,and associated reference values y for each spectrum:w=(X^(T)X)⁻¹X^(T)yW.  (5)

The modeling error that might be expected in a multivariate system usingEquation 5 can be estimated using a linear additive mixture model.Linear additive mixtures are characterized by the definition that thesum of the pure spectra of the individual constituents in a mixtureequals the spectra of the mixture. Linear mixture models are useful inassessing the general limitations of multivariate models that are basedon linear additive systems and those, noninvasive blood analysis, forexample, that can be expected to deviate somewhat from linear additivebehavior.

FIG. 4 shows an exemplary noninvasive absorbance spectrum. A set ofspectral measurements may be represented as a matrix X where each rowcorresponds to an individual sample spectrum and each column representsthe signal magnitude at a single wavelength. The measurement matrix canbe represented as a linear additive mixture model with a matrix ofinstrument baseline variations B₀, a matrix of spectra of the purecomponents K, and the concentrations of the pure components, Y, andrandom measurement noise present in the measurement of each spectrum, E.X=B₀+YK^(T)+E  (6)

The linear additive model can be broken up further into interferents andanalytes as an extended mixture model.X=B₀+YK^(T)+TP^(T)+E  (7)

In equation 4 7, T is a matrix representing the concentration ormagnitude of interferents in all samples, and P represents the purespectra of the interfering substances or effects present. Any spectraldistortion can be considered an interferent in this formulation. Forexample, the effects of variable sample scattering and deviations inoptical sampling volume must be included as sources of interference inthis formulation. The direct calibration for a generalized least squaresmodel on analyte y isy_(GLS)=(K^(T)Σ⁻¹K)⁻¹K^(T)Σ⁻¹(x−k₀);  (8)where Σ is defined as the covariance matrix of the interferingsubstances or spectral effects, ó is defined as the measurement noise, xis the spectral measurement, and k₀ is the instrument baseline componentpresent in the spectral measurement.Σ=P^(T)(tt^(T))⁻¹P+diag(ó²)  (9)

The derived mean squared error (MSE) of such a generalized least squarespredictor is found in Martens, et al., supra.MSE(y_(GLS))=trace(K^(T)Σ⁻¹K)⁻¹  (10)

Equation 10 describes the generalized limitations of least squarespredictors in the presence of interferents. If K represents theconcentrations of blood glucose, a basic interpretation of Equation 10is: the mean squared error in glucose estimates increases with increasedvariation in interferences that also co-vary with glucose concentrationin calibration data. Therefore, the accurate estimation of glucose isbest performed on measurements exhibiting smaller interferencevariations that poorly correlate with glucose concentration in thecalibration set data. Since it may not be possible to make allinterference variations random with glucose, it is desirable to limitthe range of spectral interference variation in general. The Multi-TierClassification provides a method for limiting variation of spectralinterferents by placing sample measurements into groups having a highdegree of internal consistency. Groups are defined based on a prioriknowledge of the sample, instrumental measurements at the tissuemeasurement site, and extracted features. With each successive tier,samples are further classified such that variation between spectrawithin a group is successively limited. Tissue parameters to be utilizedin class definition may include: stratum corneum hydration, tissuetemperature, and dermal thickness.

TISSUE HYDRATION

The stratum corneum (SC), or horny cell layer covers about 10-15 μmthickness of the underside of the arm. The SC is composed mainly ofkeratinous dead cells, water and some lipids. See D. Bommannan, R.Potts, R. Guy, Examination of the Stratum Corneum Barrier Function InVivo by Infrared Spectroscopy, J. Invest. Dermatol., vol. 95, pp 403-408(1990). Hydration of the SC is known to vary over time as a function ofroom temperature and relative humidity. See J. Middleton, B. Allen,Influence of temperature and humidity on stratum corneum and itsrelation to skin chapping, J. Soc. Cosmet. Chem., vol. 24, pp. 239-43(1973). Because it is the first tissue penetrated by the spectrometerincident beam, more photons sample the SC than any other part of thetissue sample. Therefore, the variation of a strong near IR absorberlike water in the first layer of the tissue sample can act to change thewavelength and depth intensity profile of the photons penetratingbeneath the SC layer.

The impact of changes in SC hydration can be observed by a simpleexperiment. In the first part of the experiment, the SC hydration isallowed to range freely with ambient conditions. In the second part ofthe experiment, variations in SC hydration are limited by controllingrelative humidity to a high level at the skin surface prior tomeasurement. Noninvasive measurements using uncontrolled and controlledhydration experiments on a single individual are plotted in FIGS. 5 and6, respectively. Changes in the water band 61 at 1900 nm can be used toassess changing surface hydration. It is apparent that the range ofvariation in the water band 61 at 1900 nm is considerably narrower inFIG. 6 than in FIG. 5. Since surface hydration represents a largevariable in the spectral measurement, it is a valuable component for usein categorizing similarity in tissue samples.

TISSUE TEMPERATURE

The temperature of the measured tissue volume varies from the core bodytemperature, at the deepest level of penetration, to the skin surfacetemperature, which is generally related to ambient temperature, locationand the amount of clothing at the tissue measurement site. The spectrumof water, which comprises about 65% of living human tissue is the mostdominant spectral component at all depths sampled in the 1100-2500 nmwavelength range. These two facts, along with the knowntemperature-induced shifting of the water band at 1450 nm, combine tosubstantially complicate the interpretation of information about manyblood analytes, including glucose. It is apparent that a range oftemperature states exist in the volume of sampled living tissue and thatthe range and distribution of states in the tissue depend on the skinsurface temperature. Furthermore, the index of refraction of skin isknown to change with temperature. Skin temperature may therefore beconsidered an important categorical variable for use in the Multi-TierClassification to identify groups for the generation of calibrationmodels and prediction.

OPTICAL THICKNESS OF DERMIS

Repeated optical sampling of the tissue is necessary to calibrate toblood constituents. Because blood represents but a part of human tissue,and blood analytes only reside in fractions of the tissue, changes inthe optical sampling of tissue may change the magnitude of the analytesignal for unchanging levels of blood analytes. This kind of a samplingeffect may confound efforts at calibration by changing the signalstrength for specific levels of analyte.

Categorization of optical sampling depth is pursued by analyzingspectral marker bands of the different layers. For example, the firsttissue layer under the skin is the subcutaneous adipose tissue,consisting mainly of fat. The strength of the fat absorbance band can beused to assess the relative photon flux that has penetrated to thesubcutaneous tissue level. A more pronounced fat band means that agreater photon flux has reached the adipose tissue and returned to thedetector. In FIG. 7, spectra with pronounced 71 and normal 72 fat bandsare presented. The most important use of the optical thickness is toassess the degree of hydration in the interior tissue sampled by theoptical probe. Optical thickness may also be a strong function of genderand body type, therefore this property measurement would be useful forassessing interior hydration states within a single individual.

The following sections describe the calibration system for the two typesof classifiers, mutually exclusive and fuzzy.

MUTUALLY EXCLUSIVE CLASSES

In the general case, the designated classification is passed to anonlinear model that provides a blood analyte prediction based on thepatient classification and spectral measurement. This process,illustrated in FIG. 8, involves the modification of the estimationstrategy for the current subject according to the structural tissueproperties and physiological state manifested in the absorbancespectrum.

This general architecture necessitates a nonlinear calibration model 101such as nonlinear partial least squares or artificial neural networkssince the mapping is highly nonlinear. The blood analyte prediction forthe preprocessed measurement x with classification specified by c isgiven byŷ=g(c,x)  (11)where g(•) is a nonlinear calibration model which maps x and c to anestimate of the blood analyte concentration, ŷ.

In the preferred realization, a different calibration is realized foreach class. The estimated class is used to select one of p calibrationmodels most appropriate for blood analyte prediction using the currentmeasurement. Given that k is the class estimate for the measurement, theblood analyte prediction isŷ=g_(k)(x),  (12)where g_(k)(•) is the calibration model associated with the k^(th)class.

The calibrations are developed from a set of exemplar absorbance spectrawith reference blood analyte values and pre-assigned classificationdefinitions. This set, denoted the “calibration set”, must havesufficient samples to completely represent the range of physiologicalstates to be encountered in the patient population. The p differentcalibration models are developed individually from the measurementsassigned to each of the p classes. The models are realized using knownmethods including principal component regression, partial least squaresregression and artificial neural networks. See Hertz, et al., supra; andPao, supra; and Haykin, supra; and Martens, et al., supra; and N.Draper, H. Smith, Applied Regression Analysis, 2^(nd) ed., John Wileyand Sons, New York (1981). The various models associated with each classare evaluated on the basis of an independent test set or crossvalidation and the “best” set of models are incorporated into theMulti-tier Classification. Each class of patients then has a calibrationmodel specific to that class.

FUZZY CLASS MEMBERSHIP

When fuzzy classification is employed the calibration is passed a vectorof memberships rather than a single estimated class. The vector, c, isutilized to determine an adaptation of the calibration model suitablefor blood analyte prediction or an optimal combination of several bloodanalyte predictions. In the general case, illustrated in FIG. 9, themembership vector and the preprocessed absorbance spectrum are both usedby a single calibration 111 for blood analyte prediction. Thecalculation is given byŷ=g(c,x)  (13)where g(•) is a nonlinear mapping determined through nonlinearregression, nonlinear partial least squares or artificial neuralnetworks. The mapping is developed from the calibration set describedpreviously and is generally complex.

The preferred realization, shown in FIG. 10, has separate calibrations121 for each class. However, each calibration is generated using allmeasurements in the calibration set by exploiting the membership vectorassigned to each measurement. In addition, the membership vector is usedto determine an optimal combination of the p blood analyte predictionsfrom all classes through defuzzification 122. Therefore, duringcalibration development, a given measurement of the calibration set hasthe opportunity to impact more than one calibration model. Similarly,during prediction more than one calibration model is used to generatethe blood analyte estimate.

Each of the p calibration models is developed using the entire set ofcalibration data. However, when the k^(th) calibration model iscalculated, the calibration measurements are weighted by theirrespective membership in the k^(th) class. As a result, the influence ofa sample on the calibration model of a particular class is a function ofits membership in the class.

In the linear case, weighted least squares is applied to calculateregression coefficients and, in the case of factor based methods, thecovariance matrix. See Duda, et al., supra. Given a matrix absorbancespectra X_(k)ε

^(rxw) and reference blood analyte concentrations Yε

^(r) where r is the number of measurement spectra and w is the numberwavelengths, let the membership in class k of each absorbance spectrumbe the elements of C_(k)ε

^(r). Then the principal components are given byF=X_(k)M,  (14)where M is the matrix of the first n eigenvectors of P. The weightedcovariance matrix P is determined throughP=X_(k)VX_(k) ^(T),  (15)where V is a square matrix with the elements of C_(k) on the diagonal.The regression matrix, B, is determined throughB=(F^(T)VF)⁻¹F^(T)VY.  (16)

When an iterative method is applied, such as artificial neural networks,the membership is used to determine the frequency the samples arepresented to the learning algorithm. Alternatively, an extended Kalmanfilter is applied with a covariance matrix scaled according to V.

The purpose of defuzzification is to find an optimal combination of thep different blood analyte predictions, based on a measurement'smembership vector that produces accurate blood analyte predictions.Therefore, defuzzification is a mapping from the vector of blood analytepredictions and the vector of class memberships to a single analyteprediction. The defuzzifier can be denoted as transformation such thatŷ=d(c,[y₁y₂y₃ . . . y_(p)]),  (17)where d(•) is the defuzzification function, c is the class membershipvector and y_(k) is the blood analyte prediction of the k^(th)calibration model. Existing methods of defuzzification, such as thecentroid or weighted average, are applied for small calibration sets.However, if the number of samples is sufficient, d(•) is generatedthrough a constrained nonlinear model.INSTRUMENT DESCRIPTION

The Multi-tiered Classification and Calibration is implemented in ascanning spectrometer which determines the NIR absorbance spectrum ofthe subject forearm through a diffuse reflectance measurement. Theinstrument employs a quartz halogen lamp, a monochromator, and InGaAsdetectors. The detected intensity from the sample is converted to avoltage through analog electronics and digitized through a 16-bit A/Dconverter. The spectrum is passed to the Intelligent Measuring System(IMS) for processing and results in either a glucose prediction or amessage indicating an invalid scan.

Although the invention is described herein with reference to thepreferred embodiment, one skilled in the art will readily appreciatethat other applications may be substituted for those set forth hereinwithout departing from the spirit and scope of the present invention.Accordingly, the invention should only be limited by the claims includedbelow.

1. A method of developing a multi-tiered calibration model forestimating concentration of a target blood analyte from measured tissuespectra, comprising the steps of: providing a calibration set, whereinsaid calibration set comprises a data set of exemplar spectralmeasurements from a representative sampling of a subject population;initially, classifying said exemplar measurements into previouslydefined classes based on a priori a priori information pertaining to acorresponding subject; further classifying said exemplar measurementsinto previously defined classes based on at least one instrumentalmeasurement at a tissue measurement site; extracting at least onefeature from said exemplar measurements for still furtherclassification, wherein a decision rule makes class assignments; andcalculating at least one localized calibration model based on saidclassified measurements and an associated set of reference values. 2.The method of claim 1, wherein said initial classification stepcomprises the steps of: in a first tier, classifying said measuredspectrum exemplar measurements into previously defined classes based onsubject's age; and in a second tier, further classifying said measuredspectrum exemplar measurements into previously defined classes based onsubject's sex.
 3. The method of claim 1, wherein said furtherclassification step further comprises the steps of: in a third tierfurther classsifying said exemplar measurements into previously definedclasses based on an estimation of stratum corneum hydration at saidtissue measurement site; and in a fourth tier, further classifying saidexemplar measurements into previously defined classes based on skintemperature at said tissue measurement site.
 4. The method of claim 3,wherein said stratum corneum hydration estimate is based on ameasurement of ambient humidity at said tissue measurement site.
 5. Themethod of claim 1, wherein said feature extraction step comprises anymathematical transformation that enhances a quality or aspect of samplemeasurement for interpretation to represent concisely structuralproperties and physiological state of a tissue measurement site, whereina resulting set of features is used to classify a subject and determinea calibration model that is most useful for blood analyte prediction. 6.The method of claim 5, wherein said features are represented in avector, zΣ

^(M) that is determined from a preprocessed measurement through:z=f(λ,x) where f(•):

^(N)→

^(M) is a mapping from a measurement space to a feature space, whereindecomposing f(•) yields specific transformations, f_(i)(•):

^(N)→

^(M) _(i) for determining a specific feature, wherein the dimensionM_(i) indicating whether an i^(th) feature is a scalar or a vector andan aggregation of all features is the vector z, and wherein a featureexhibits a certain structure indicative of an underlying physicalphenomenon when said feature is represented as a vector or a pattern. 7.The method of claim 6, wherein individual features are divided intocategories, said categories comprising: abstract features that do notnecessarily have a specific interpretation related to a physical system;and simple features that are derived from an a priori understanding of asample and that can be related directly to a physical phenomenon.
 8. Themethod of claim 7, wherein said simple features can be calculated fromNIR spectral absorbance measurements, said simple features including anyof: thickness of adipose tissue; hematocrit level; tissue hydration;magnitude of protein absorbance; scattering properties of said tissue;skin thickness; temperature related effects; age related effects;spectral characteristics; pathlength estimates; volume fraction of bloodin tissue; and spectral characteristics related to environmentalinfluences.
 9. The method of claim 1, further comprising the step of:employing spectral decomposition to determine features related to aknown spectral absorbance pattern.
 10. The method of claim 1, furthercomprising the step of: employing factor-based methods to build a modelcapable of representing variation in a measured absorbance spectrumrelated to a demographic variable; wherein projection of a measuredabsorption onto said model constitutes a feature that representsspectral variation related to said demographic variable.
 11. The methodof claim 1, wherein said feature extraction step assigns a measurementto one of many predefined classes.
 12. The method of claim 1, furthercomprising the steps of; measuring the similarity of a feature topredefined classes; and assigning class membership.
 13. The method ofclaim 1, further comprising the step of; using measurements and classassignments to determine a mapping from features to class assignments.14. The method of claim 13, further comprising the steps of: definingclasses from said features in a supervised manner, wherein each set offeatures is divided into two or more regions, and wherein classes aredefined by combination of feature divisions; performing a clusteranalysis on the spectral data to determine groups of said definedclasses that can be combined, wherein the final number of classdefinitions is significantly reduced; designing a classifier subsequentto class definition through supervised pattern recognition bydetermining an optimal mapping or transformation from the feature spaceto a class estimate that minimizes the number of misclassifications; andcreating a model based on class definitions that transforms a measuredset of features to an estimated classification, wherein said classdefinitions are optimized to satisfy specifications of a measurementsystem used to take said measurements.
 15. The method of claim 14,wherein said optimized classes comprise groups of measurements whereinsimilarity between measurements within a group is greater thansimilarity between groups.
 16. The method of claim 15, said step ofcalculating at least one localized calibration model comprising:calculating weights, w, for said exemplar measurements according to:W=(X^(T)X)⁻¹X^(T)y,  where X represents a matrix of spectralmeasurements, and y represents a reference value of said target analyteconcentration for each measurement.
 17. The method of claim 16, whereina vector of weights of spectral measurements within one of said groupscomprises a regression vector for said group; wherein said regressionvector comprises a calibration model for said group.
 18. A method ofdeveloping a multi-tiered calibration model for estimating concentrationof a target blood analyte from measured tissue spectra, comprising thesteps of: providing a calibration set, wherein said calibration setcomprises a data set of exemplar spectral measurements from arepresentative sampling of a subject population; in at least one tier,classifying said exemplar measurements into previously defined classes;and extracting at least one feature from said exemplar measurements forstill further classification; and calculating at least one localizedcalibration model based on said classified exemplar measurements and aset of associated reference values.
 19. The method of claim 18, whereinsaid classifying step is based on any of: abstract and simple features.20. The method of claim 18, further comprising the step of mapping saidexemplar measurements to estimates of said analyte based on either alinear or a nonlinear model.
 21. The method of claim 18, wherein saidclassifying step is based on any of: a prioria priori information; andat least one instrumental measurement at a tissue measurement site atwhich optical samples were taken for said spectral measurements.
 22. Themethod of claim 18, wherein said classifying step comprises multipletiers.
 23. The pattern classification method of claim 22, wherein saidclassifying step comprises any of the steps of: classifying saidexemplar measurements into previously defined classes based on subject'sage; classifying said exemplar measurements into previously definedclasses based on subject's sex; classifying said exemplar measurementsinto previously defined classes based on an estimation of stratumcorneum hydration of said tissue measurement site; and classifying saidexemplar measurements into previously defined classes based on skintemperature at said tissue measurement site.
 24. A method for developinga calibration model for estimating a target analyte property frommeasured tissue spectra, comprising the steps of: providing a data setof exemplar spectral measurements from a sampling of a subjectpopulation; classifying a majority of said exemplar measurements intoclasses using at least one feature of said exemplar measurements;wherein said feature comprises a spectral feature, wherein said classescomprise groups of measurements wherein similarity between measurementswithin a group is greater than similarity between groups, andcalculating at least one localized calibration model using saidclassified measurements and an associated set of reference values. 25.The method of claim 24, wherein said classifying step comprisesclassifying based on any of: a priori information; a physicalmeasurement; and an optical measurement at a tissue measurement site.26. The method of claim 25, wherein said a priori information comprisesany of: age; gender; hematocrit level; and temperature.
 27. The methodof claim 25, wherein said physical measurement comprises any of:thickness of adipose tissue; tissue hydration; scattering properties ofsaid tissue; and skin thickness.
 28. The method of claim 25, whereinsaid optical measurement comprises any of: magnitude of proteinabsorbance; magnitude of fat absorbance; a spectral characteristic; apathlength estimate; volume fraction of blood in tissue; and a spectralfeature.
 29. The method of claim 25, wherein said classes at leastpartially share exemplar measurements.
 30. The method of claim 25,further comprising the step of: assigning degree of membership to atleast some of said exemplar measurements according to a fuzzy membershipfunction.
 31. The method of claim 30, wherein at least one of saidlocalized calibration models comprises coefficients calculated withexemplar measurements and said degree of membership.
 32. The method ofclaim 31, further comprising the steps of: providing an estimationspectrum; assigning degree of class membership to said estimationspectrum in at least one of said classes; estimating at least oneinterim analyte property with said localized calibration models; andcombining said estimates to determine said analyte property.
 33. Themethod of claim 32, wherein said step of assigning comprises use of afuzzy membership function.
 34. The method of claim 32, wherein said stepof combining uses said degree of class membership.
 35. The method ofclaim 24, wherein said classifying step comprises: classifying saidexemplar measurements into previously defined classes based on at leastone instrument measurement at a tissue measurement site.
 36. The methodof claim 24, wherein said feature extraction comprises the steps of:representing structural properties and physiological state of a tissuemeasurement site through application of at least one mathematicaltransformation that enhances a quality or aspect of sample measurementfor interpretation, and using a resulting set of features i to classifya subject and determine a calibration model that is most useful forblood analyte prediction.
 37. The method of claim 36, wherein said stepof representing structural properties and physiological state comprisesthe step of: representing features in a vector, zε ^(M) that isdetermined from a preprocessed measurement through:z=f(λ,x)  where f: ^(N) → ^(M) is a mapping space to a feature space,wherein decomposing f(•) yields specific transformations, f _(i)(•):

^(N) → ^(M) _(i) for determining a specific feature, wherein thedimension M _(i) indicates whether an i ^(th) feature is a scalar or avector and an aggregation of all features is the vector z.
 38. Themethod of claim 24, wherein said feature exhibits a structure indicativeof an underlying physical phenomenon when said feature is represented asa vector or a pattern.
 39. The method of claim 24, wherein said featurecomprises any of: a simple feature; and an abstract feature.
 40. Themethod of claim 24, wherein a decision rule makes class assignments. 41.The method of claim 24, wherein said features comprise sets of featuresand wherein the step of defining classes in a supervised mannercomprises the steps of: dividing each set of features into two or moreregions, wherein classes are defined by combinations of featuredivisions, wherein classes are defined through known differences indata; performing a cluster analysis on the exemplar measurements todetermine groups of said defined classes that can be combined to reducethe final number of class definitions; designing a classifier subsequentto class definition through supervised pattern recognition bydetermining an optimal mapping or transformation from the feature spaceto a class estimate that minimizes the number of misclassifications; andcreating a model based on class definitions that transforms a measuredset of features to an estimated classification, wherein said classdefinitions are optimized to satisfy specifications of a measurementsystem used to take said measurements.
 42. The method of claim 41,further comprising: calculating weights, W, for said measurements,according to:W=(X ^(T) X)⁻¹ X ^(T) Y,  where X represents a matrix of measurements,and Y represents a reference value of a target analyte concentration foreach measurement.
 43. The method of claim 42, wherein a vector ofweights of spectral measurements within one of said groups comprises aregression vector for said group; and wherein said regression vectorcomprises a calibration model for said group.
 44. The method of claim24, wherein the steps of defining said classes in an unsupervised mannercomprises: developing clusters of data in feature space based on themeasurements, wherein within-cluster homogeneity and between-clusterseparation is maximized.
 45. The method of claim 44, wherein clustersformed from features having physical meaning are interpreted based onthe known underlying phenomenon causing variation in the feature space.46. The method of claim 24, wherein said classes are defined on thebasis of structural and state similarity, wherein variation in tissuecharacteristics within a class is smaller than the variation betweenclasses.
 47. The method of claim 24, wherein said classifying step isbased on any of: a simple feature; and an abstract feature.
 48. Themethod of claim 24, further comprising the step of: preprocessing priorto said step of classifying.
 49. A method for developing a calibrationmodel for estimating a target analyte property from measured tissuespectra, comprising the steps of: providing a data set of exemplarspectral measurements from a sampling of a subject population;classifying a majority of said exemplar measurements into classes usingat least one feature of said exemplar measurements; and calculating atleast one localized calibration model using said classified measurementsand an associated set of reference values, wherein the step ofclassifying comprises classifying through at least two tiers.
 50. Amethod for developing a calibration model for estimating a target bloodanalyte property from measured tissue spectra, comprising the steps of:providing a calibration set, wherein said calibration set comprises adata set of exemplar spectral measurements from a representativesampling of a subject population; extracting at least one feature fromat least one of said exemplar measurements; classifying at least aportion of said exemplar measurements into classes using said feature;and calculating at least one localized calibration model for at leastone of said classes based on said classified measurements and anassociated set of reference values, wherein said step of extracting atleast one feature comprises: representing structural properties andphysiological state of a tissue measurement site through application ofat least one mathematical transformation that enhances a quality oraspect of sample measurement for interpretation, wherein a resulting setof features is used to classify a subject and determine a calibrationmodel.
 51. The method of claim 50, wherein said feature comprises aspectral feature.
 52. The method of claim 50, wherein the step ofclassifying comprises classifying based on any of: a priori information;a physical measurement; and an optical measurement of a tissuemeasurement site.
 53. The method of claim 50, wherein the step ofclassifying measurements comprises: classifying said exemplarmeasurements into previously defined classes based on at least oneinstrument measurement at a tissue measurement site.
 54. The method ofclaim 50, wherein said feature comprises any of: a simple feature; andan abstract feature.
 55. The method of claim 50, wherein the step ofclassifying comprises classifying said exemplar measurements, whereinsaid classes are defined in any of supervised and unsupervised manners.56. The method of claim 50, wherein the step of extracting comprises amathematical transformation resulting in any of: a simple feature; andan abstract feature.
 57. The method of claim 50, wherein said classes atleast partially share exemplar measurements.
 58. The method of claim 50,wherein the step of classifying comprises classifying through at leasttwo tiers.
 59. The method of claim 50, wherein said classes arepreviously defined.
 60. The method of claim 50, further comprising thestep of: preprocessing prior to said step of extracting.
 61. The methodof claim 50, wherein the step of classifying uses any of: a crispfunction; and a fuzzy function.
 62. A method for developing acalibration algorithm for calculating concentration of a target bloodanalyte from measured tissue spectra, comprising the steps of: providinga data set of exemplar spectral measurements from a representativesampling of a subject population; classifying at least one of saidexemplar measurements into previously defined classes; and calculatingat least one localized calibration model using said classifiedmeasurements and an associated set of reference values, wherein saidclasses comprise groups of measurements, wherein similarity betweenmeasurements within a group is greater than similarity between groups.63. The method of claim 62, wherein said classes are defined by any of:a priori information; a physical measurement; and an optical measurementat a tissue measurement site.
 64. The method of claim 63, wherein said apriori information comprises any of: age; gender; hematocrit level; andtemperature.
 65. The method of claim 63, wherein said physicalmeasurement comprises any of: thickness of adipose tissue; tissuehydration; scattering properties of said tissue; and skin thickness. 66.The method of claim 63, wherein said optical measurement comprises anyof: magnitude of protein absorbance; magnitude of fat absorbance; aspectral characteristic; a pathlength estimate; volume fraction of bloodin tissue; and a spectral feature.
 67. The method of claim 62, wherein adecision rule makes class assignments.
 68. A method for developing amulti-tier calibration model for determining concentration of a targetblood analyte from measured tissue spectra, comprising the steps of:providing a calibration set, wherein said calibration set comprises adata set of exemplar spectral measurements from a representativesampling of a subject population; through at least two tiers,classifying said exemplar measurements into classes; and calculating atleast one localized calibration model using said classified measurementsand an associated set of reference values.